Facet Recommender Sentiment Analysis for Conversational Topics
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Solution Overview
Problem
Existing recommendation systems lack the ability to provide nuanced and contextually relevant recommendations for specific facets of conversational topics, such as restaurant characteristics, based on sentiment-bearing content like consumer reviews and social media, which limits their effectiveness in guiding user decisions.
Innovation Solution
The Facet Recommender employs machine-learned facet and sentiment models to identify and analyze sentiment-bearing content, generating conversational utterances that reflect consensus sentiments and characteristics of conversational topics, allowing for personalized and context-aware recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collaborative filtering or content-based filtering mechanisms are used to construct recommendation models, then recommendations can be generated based on user behaviors or item characteristics, but the system lacks the ability to provide nuanced recommendations for specific facets of conversational topics based on sentiment-bearing content
Solution Approach 1:
The patent segments the recommendation system into multiple specialized models: a topic model that identifies conversational topics from sentiment-bearing content, a facet model that extracts specific facets (attributes) of those topics, and a sentiment model that determines emotional tone. This segmentation allows the system to provide nuanced recommendations for specific facets while preserving sentiment information, as each model processes and preserves specific aspects of the input data rather than treating all information uniformly.
2Measurement precision
If the system analyzes sentiment-bearing content to identify conversational topics and facets, then contextually relevant recommendations can be provided, but the complexity of the recommendation system increases
Solution Approach 1:
The patent introduces an intermediary processing layer between the input sentiment-bearing content and the final recommendations. This layer includes a topic model that acts as a mediator to identify conversational topics, a facet model that mediates the extraction of specific facets, and a sentiment model that mediates the determination of emotional tone. These intermediary models break down the complex task of sentiment analysis into manageable stages, improving contextual relevance while managing system complexity through modular design.
3Adaptability or versatility
If machine-learned facet and sentiment models are employed to identify and analyze sentiment-bearing content, then personalized and context-aware recommendations can be generated, but the processing time and computational resources increase
Solution Approach 1:
The patent employs preliminary action by pre-training the topic model, facet model, and sentiment model on large datasets before deployment. This pre-training establishes foundational knowledge structures that enable the models to quickly process new sentiment-bearing content without requiring extensive computation during actual recommendation generation. The models have already learned to identify topics, facets, and sentiments from training data, so they can efficiently apply this knowledge to generate personalized recommendations in real-time or near-real-time scenarios.
Data Source
AI summary
A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks. Further, responses or suggestions provided as utterances may be personalized to individual users.